About this Research Topic
The conclusion of the Genome Project in 2003, where a team of geneticists and computer scientists from different countries of the world produced the first read of human genome, has opened a revolutionary era in the scientific research, economics and clinical practices.
The USA President, Barack Obama, commented ten years later, the pioneering results of the project stating “if we want to make the best products, we also have to invest in the best ideas. Every dollar we invested to map the human genome returned $140 to the economy—every dollar.” The message behind these words was strong and valid. Countries such as Canada (2005), USA (2008-2015), UK (2013), and Japan (2014) started investing considerable resources and long term projects to construct sequenced-genome-banks of their populations. Other countries are evaluating the possibility to make similar investments, since, now, there is a common awareness that the current public-healthy-systems and the clinical practices are intended to be supplanted by the genome-based public health systems and clinical practices.
However, the prediction of the diseases to which we can run, the understanding of how we respond to treatments, and the determination of the right doses of drugs that must be taken to get benefits, and not be killed by their own side effects, can be achieved by analyzing the genomes of a large number of individuals (in sickness and in health, youth, elderly, infants, with different lifestyles and ethnic groups). From a conceptual point of view, two different, although related kinds of analysis, were intensively developed in the last years: genome sequence analyses and genome expression analyses, based on transcriptomes. New steps toward innovative methods in both of them, their integration, and their medical applications, are one of the most urgent challenges in life science.
On the other hand, in agriculture there is a long history in experiencing the traditional strategies to improve production, protecting it from pests and diseases. Traditional methods can be described as the genetic combination, on the basis of phenotypic analysis of plants and the selection among the resulting offspring of those which show the phenotype with better characteristics and superior performance. To date, those methods are weak to ensure product quality with the optimization of the investment costs of the manufacturing companies and its their competitiveness in the market. Being the economy in most regions in the world based on agriculture, the traditional methods must be replaced by emerging predictive methods, more specific and reliable, based on genomic and transcriptomic analyses as suggested by successful project such as, among others, RosBREED, combining disease resistance with horticultural quality in rosaceae cultivars.
This Research Topic is devoted to bioinformaticians, geneticists and researchers who study or apply methods and algorithms for genomes and transcriptome analyses, aimed at understanding pathology discriminations and classifications. Papers are encouraged that use unconventional approaches and/or where mathematical and computational concepts are applied to biological and medical contexts in original ways. The contributions presented in the Topic should be of interest also to a wide class of scientists and students involved in the several fields where genomic and transcriptomic approaches are becoming essential for future investigations.
This Research Topic welcomes Original Research, Review, Clinical Trial Method, and Perspective articles.
The USA President, Barack Obama, commented ten years later, the pioneering results of the project stating “if we want to make the best products, we also have to invest in the best ideas. Every dollar we invested to map the human genome returned $140 to the economy—every dollar.” The message behind these words was strong and valid. Countries such as Canada (2005), USA (2008-2015), UK (2013), and Japan (2014) started investing considerable resources and long term projects to construct sequenced-genome-banks of their populations. Other countries are evaluating the possibility to make similar investments, since, now, there is a common awareness that the current public-healthy-systems and the clinical practices are intended to be supplanted by the genome-based public health systems and clinical practices.
However, the prediction of the diseases to which we can run, the understanding of how we respond to treatments, and the determination of the right doses of drugs that must be taken to get benefits, and not be killed by their own side effects, can be achieved by analyzing the genomes of a large number of individuals (in sickness and in health, youth, elderly, infants, with different lifestyles and ethnic groups). From a conceptual point of view, two different, although related kinds of analysis, were intensively developed in the last years: genome sequence analyses and genome expression analyses, based on transcriptomes. New steps toward innovative methods in both of them, their integration, and their medical applications, are one of the most urgent challenges in life science.
On the other hand, in agriculture there is a long history in experiencing the traditional strategies to improve production, protecting it from pests and diseases. Traditional methods can be described as the genetic combination, on the basis of phenotypic analysis of plants and the selection among the resulting offspring of those which show the phenotype with better characteristics and superior performance. To date, those methods are weak to ensure product quality with the optimization of the investment costs of the manufacturing companies and its their competitiveness in the market. Being the economy in most regions in the world based on agriculture, the traditional methods must be replaced by emerging predictive methods, more specific and reliable, based on genomic and transcriptomic analyses as suggested by successful project such as, among others, RosBREED, combining disease resistance with horticultural quality in rosaceae cultivars.
This Research Topic is devoted to bioinformaticians, geneticists and researchers who study or apply methods and algorithms for genomes and transcriptome analyses, aimed at understanding pathology discriminations and classifications. Papers are encouraged that use unconventional approaches and/or where mathematical and computational concepts are applied to biological and medical contexts in original ways. The contributions presented in the Topic should be of interest also to a wide class of scientists and students involved in the several fields where genomic and transcriptomic approaches are becoming essential for future investigations.
This Research Topic welcomes Original Research, Review, Clinical Trial Method, and Perspective articles.
Important Note: All contributions to this Research Topic must be within the scope of the section and journal to which they are submitted, as defined in their mission statements. Frontiers reserves the right to guide an out-of-scope manuscript to a more suitable section or journal at any stage of peer review.
